In Situ Detection and Measurement of Broccoli Heads Under Different Lighting Conditions Using Proximal Remote Sensing

ABSTRACT Broccoli head recognition algorithms often demonstrate poor robustness under complex natural lighting conditions. Additionally, commonly used instance segmentation methods require extensive manual annotation, making their application to other broccoli datasets challenging. Improving detection accuracy and reducing annotation costs are crucial for efficient in‐field broccoli head recognition. We aimed to develop a robust, transferable broccoli head detection and diameter estimation algorithm that performs accurately under complex lighting conditions and leaf occlusion. We proposed the RSDF algorithm, integrating YOLOv8n for object detection, SAM2 for segmentation, and depth‐based contour denoising. Field tests were conducted under mixed lighting conditions with shading and auxiliary illumination. The proposed RSDF algorithm demonstrated high robustness, maintaining a recognition rate above 95% across different lighting conditions. Under shading conditions, the segmentation performance of SAM2 remained stable, with a coefficient of variation within 3% despite changes in auxiliary lighting intensity. Testing the RSDF algorithm on 100 field images with shaded auxiliary lighting yielded a detection accuracy of 96.9% and a head diameter estimation accuracy of 90.31%. The RSDF algorithm enhances broccoli detection in challenging environments, offering a scalable solution for automated harvesting.